app.py

import torch
from PIL import Image
import gradio as gr
from sam2.sam2_image_predictor import SAM2ImagePredictor

# Load the SAM2 model
predictor = SAM2ImagePredictor.from_pretrained("facebook/sam2.1-hiera-large")

# Function to predict masks from the image and prompts
def generate_mask(image, prompt):
    with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
        predictor.set_image(image)
        masks, _, _ = predictor.predict(prompt)
    return masks[0]  # Returning the first mask for simplicity

# Set up the Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Image Segmentation using SAM2")

    # Input: Upload an image
    image_input = gr.Image(label="Upload Image", type="pil")

    # Input: Text prompt for image segmentation
    prompt_input = gr.Textbox(label="Enter segmentation prompt", placeholder="Describe what you want to segment")

    # Output: Display the mask generated by the SAM2 model
    output_mask = gr.Image(label="Generated Mask")

    # Button to trigger mask generation
    generate_button = gr.Button("Generate Mask")

    # Link button click with the segmentation function
    generate_button.click(fn=generate_mask, inputs=[image_input, prompt_input], outputs=output_mask)

# Launch the Gradio app
demo.launch()